code: def predict_poses(self, inputs):
      outputs = {}
  
      pose_feats = {
          f_i: inputs["color_aug", f_i, 0] for f_i in self.opt.frame_ids
      }
  
      for f_i in self.opt.frame_ids[1:]:
          if f_i != "s":
              # To maintain ordering we always pass frames in temporal order
              if f_i < 0:
                  pose_inputs = [pose_feats[f_i], pose_feats[0]]
              else:
                  pose_inputs = [pose_feats[0], pose_feats[f_i]]
  
              axisangle, translation = self.posenet(
                  mx.nd.concat(*pose_inputs, dim=1)
              )
              outputs[("axisangle", 0, f_i)] = axisangle
              outputs[("translation", 0, f_i)] = translation
  
              # Invert the matrix if the frame id is negative
              outputs[
                  ("cam_T_cam", 0, f_i)
              ] = transformation_from_parameters(
                  axisangle[:, 0], translation[:, 0], invert=(f_i < 0)
              )
  
      return outputs
  
imports: from __future__ import absolute_import, division, print_function
  import os
  import sys
  import shutil
  import copy
  from tqdm import tqdm
  import numpy
  import json
  import mxnet
  from mxnet import gluon, autograd
  from gluoncv.data import KITTIRAWDataset, KITTIOdomDataset
  from gluoncv.data.kitti.kitti_utils import dict_batchify_fn, readlines
  from gluoncv.model_zoo import get_model
  from gluoncv.model_zoo.monodepthv2.layers import *
  from gluoncv.model_zoo.monodepthv2 import MonoDepth2PoseNet
  from gluoncv.utils import LRScheduler, LRSequential
label_desc: Use image node_id 6 for calling the Trainer obj's underlying member method code with example usage: obj.predict_poses(inputs) and returns: outputs